Online scaled gradient methods adapt matrix step sizes via online learning, match the best fixed step size asymptotically, and achieve non-asymptotic superlinear convergence on smooth strongly convex problems.
Online learning rate adaptation with hypergradient descent
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Gradient Methods with Online Scaling Part I. Theoretical Foundations
Online scaled gradient methods adapt matrix step sizes via online learning, match the best fixed step size asymptotically, and achieve non-asymptotic superlinear convergence on smooth strongly convex problems.